Search Results - (((((((want OR semantic) OR mantis) OR hands) OR cantor) OR anne) OR file) OR wanting) algorithms.

Simple adaptive strategies : from regret-matching to uncoupled dynamics by Hart, Sergiu

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Nearest-neighbor methods in learning and vision : theory and practice

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Error Correction Coding : Mathematical Methods and Algorithms. by Moon, Todd K.

Table of Contents: “…Cover -- Title Page -- Copyright -- Contents -- Preface -- List of Program Files -- List of Laboratory Exercises -- List of Algorithms -- List of Figures -- List of Tables -- List of Boxes -- About the Companion Website -- Part I Introduction and Foundations -- Chapter 1 A Context for Error Correction Coding -- 1.1 Purpose of This Book -- 1.2 Introduction: Where Are Codes? …”
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Computer animation : algorithms and techniques by Parent, Rick

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Building winning algorithmic trading systems : a trader's journey from data mining to Monte Carlo simulation to live trading by Davey, Kevin J., 1966-

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Hands-On Deep Learning with TensorFlow. by Boxel, Dan Van

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Network and discrete location : models, algorithms, and applications by Daskin, Mark S., 1952-

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Algorithms, architectures and information systems security

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Learning Neo4j 3.x - Second Edition. by Baton, Jerome

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Learning JavaScript data structures and algorithms : understand and implement classic data structures and algorithms using JavaScript by Groner, Loiane

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Boosting : foundations and algorithms by Schapire, Robert E.

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Molecular bioinformatics : algorithms and applications by Schulze-Kremer, S. (Steffen)

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Hands-On Artificial Intelligence for IoT : Expert Machine Learning and Deep Learning Techniques for Developing Smarter IoT Systems. by Kapoor, Amita

Table of Contents: “…Using TXT files in PythonCSV format; Working with CSV files with the csv module; Working with CSV files with the pandas module; Working with CSV files with the NumPy module; XLSX format; Using OpenPyXl for XLSX files; Using pandas with XLSX files; Working with the JSON format; Using JSON files with the JSON module; JSON files with the pandas module; HDF5 format; Using HDF5 with PyTables; Using HDF5 with pandas; Using HDF5 with h5py; SQL data; The SQLite database engine; The MySQL database engine; NoSQL data; HDFS; Using hdfs3 with HDFS; Using PyArrow's filesystem interface for HDFS; Summary…”
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A Hands-On Guide to Designing Embedded Systems. by Taylor, Adam

Table of Contents: “…-- 3.6.2 Self-Checking Test Benches -- 3.6.3 Corner Cases, Boundary Conditions, and Stress Testing -- 3.6.4 Code Coverage -- 3.6.5 Test Functions and Procedures -- 3.6.6 Behavioral Models -- 3.6.7 Using Text IO Files -- 3.6.8 What Else Might We Consider? -- 3.7 Finite State Machine Design -- 3.7.1 Defining a State Machine -- 3.7.2 Algorithmic State Diagrams -- 3.7.3 Moore or Mealy: What Should I Choose? …”
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Deep Learning for the Earth Sciences : A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences. by Camps-Valls, Gustau

Table of Contents: “…6.1.4 Evaluation Metrics -- 6.1.4.1 Precision-Recall Curve -- 6.1.4.2 Average Precision and Mean Average Precision -- 6.1.5 Applications -- 6.2 Preliminaries on Object Detection with Deep Models -- 6.2.1 Two-stage Algorithms -- 6.2.1.1 R-CNNs -- 6.2.1.2 R-FCN -- 6.2.2 One-stage Algorithms -- 6.2.2.1 YOLO -- 6.2.2.2 SSD -- 6.3 Object Detection in Optical RS Images -- 6.3.1 Related Works -- 6.3.1.1 Scale Variance -- 6.3.1.2 Orientation Variance -- 6.3.1.3 Oriented Object Detection -- 6.3.1.4 Detecting in Large-size Images -- 6.3.2 Datasets and Benchmark -- 6.3.2.1 DOTA -- 6.3.2.2 VisDrone…”
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Hands-On Reinforcement Learning with Python : Master Reinforcement and Deep Reinforcement Learning Using OpenAI Gym and TensorFlow. by Ravichandiran, Sudharsan

Table of Contents: “…Solving the taxi problem using Q learningSARSA; Solving the taxi problem using SARSA; The difference between Q learning and SARSA; Summary; Questions; Further reading; Chapter 6: Multi-Armed Bandit Problem; The MAB problem; The epsilon-greedy policy; The softmax exploration algorithm; The upper confidence bound algorithm; The Thompson sampling algorithm; Applications of MAB; Identifying the right advertisement banner using MAB; Contextual bandits; Summary; Questions; Further reading; Chapter 7: Deep Learning Fundamentals; Artificial neurons; ANNs; Input layer; Hidden layer; Output layer.…”
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Hands-on ensemble learning with Python : build highly optimized ensemble machine learning models using scikit-learn and Keras by Kyriakides, George, Margaritis, Konstantinos G.

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Hands-On Automated Machine Learning : a beginner's guide to building automated machine learning systems using AutoML and Python. by Das, Sibanjan

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Hands-on recommendation systems with Python : start building powerful and personalized, recommendation engines with Python by Banik, Rounak

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Delphi High Performance : Build fast Delphi applications using concurrency, parallel programming and memory management. by Gabrijelčič, Primož

Table of Contents: “…Object file formatsObject file linking in practice; Using C++ libraries; Using a proxy DLL in Delphi; Summary; Chapter 9: Best Practices; About performance; Fixing the algorithm; Fine-tuning the code; Memory management; Getting started with the parallel world; Working with parallel tools; Exploring parallel practices; Using external libraries; Final words; Other Books You May Enjoy; Index.…”
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